@COMMENT This file was generated by bib2html.pl <https://sourceforge.net/projects/bib2html/> version 0.94
@COMMENT written by Patrick Riley <http://sourceforge.net/users/patstg/>
@COMMENT This file came from Kuldeep S. Meel's publication pages at
@COMMENT http://www.comp.nus.edu.sg/~meel/publications/
@inproceedings{CMMV15,
  title={Approximate Probabilistic Inference via Word-Level Counting},
  bib2html_dl_pdf={../Papers/AAAI16.pdf},
  bib2html_rescat={Counting},
  code={https://bitbucket.org/kuldeepmeel/smtapproxmc},
  author={Chakraborty, Supratik and Meel, Kuldeep S. and Mistry, Rakesh and Vardi, Moshe Y.},
  year={2016},
  month=jun,
  booktitle=AAAI,
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Counting},
  abstract={Hashing-based model counting has emerged as a promising
approach for large-scale probabilistic inference on graphical
models. A key component of these techniques is the
use of xor-based 2-universal hash functions that operate over
Boolean domains. Many counting problems arising in probabilistic
inference are, however, naturally encoded over fi-
nite discrete domains. Techniques based on bit-level (or
Boolean) hash functions require these problems to be propositionalized,
making it impossible to leverage the remarkable
progress made in SMT (Satisfiability Modulo Theory)
solvers that can reason directly over words (or bit-vectors). In
this work, we present the first approximate model counter that
uses word-level hashing functions, and can directly leverage
the power of sophisticated SMT solvers. Empirical evaluation
over an extensive suite of benchmarks demonstrates the
promise of the approach.
	},
}